Data Science x Public Health

This Is Why Resource Allocation Models Don’t Work (And Nobody Talks About It)

Resource allocation models are supposed to help public health systems distribute scarce resources more intelligently. They promise better targeting, more efficient deploy

Everyone Uses Censoring Assumptions… But They Fail When Leaving the Study Is Part of the Outcome

Censoring is one of the most common assumptions in epidemiology and survival analysis. It is often treated as a routine technical step for handling people who leave obser

In Theory, Model Averaging Works. In Reality… It Doesn’t

Model averaging is often presented as a more careful and uncertainty-aware alternative to choosing one model specification. It is supposed to reduce overconfidence and ma

In Theory, Real-Time Health Alerts Work. In Reality… They Don’t

Real-time health alerts are supposed to detect danger faster and trigger earlier intervention. They promise speed, precision, and smarter public health response. But wha

This Is Why Competing Risks Don’t Work (And Nobody Talks About It)

Competing risks methods are often presented as a more realistic way to analyze time-to-event data in epidemiology and public health. They promise to handle situations whe

In Theory, External Validation Works. In Reality… It Doesn’t

External validation is often presented as the gold standard for proving that a predictive model works beyond its original dataset. It is supposed to show that the model c

Everyone Uses Public Health Scorecards… But They Fail When the Incentive Is the Metric

Public health scorecards are supposed to improve accountability and make system performance easier to track. They promise clarity, targets, and faster decision-making. B

Everyone Uses Attack Rates… But They Fail When Exposure Isn’t Shared

Attack rates are one of the most common tools in outbreak epidemiology. They seem to offer a quick answer to a simple question: how many exposed people got sick? But what

This Is Why Adjustment for Baseline Differences Doesn’t Work (And Nobody Talks About It)

Adjustment for baseline differences is one of the most common moves in health research and biostatistics. It is often treated as proof that two groups have been made more

Everyone Uses AI Triage Tools… But They Fail When the Health System Is the Real Problem

AI triage tools are designed to identify high-risk patients and communities faster. They promise smarter prioritization, earlier intervention, and more efficient care del

You’ve Been Using Secondary Attack Rates Wrong — Here’s What Actually Happens

Secondary attack rates are often used to estimate how infection spreads among close contacts. They seem to provide a focused measure of transmission in households, school

Everyone Uses Sensitivity Analyses… But They Fail When the Assumption Space Is Too Small

Sensitivity analyses are often presented as proof that a result is robust and trustworthy. They are supposed to show that findings hold up even when assumptions are chang

This Is Why Health Equity Dashboards Don’t Work (And Nobody Talks About It)

Health equity dashboards are supposed to make disparities visible and drive better public health decisions. They promise transparency, accountability, and measurable prog

You’ve Been Using Prevalence Wrong — Here’s What Actually Happens

Prevalence is one of the most commonly used measures in epidemiology. It is often treated as a direct indicator of disease risk, spread, or public health urgency. But wha

You’ve Been Using Statistical Power Wrong — Here’s What Actually Happens

Statistical power is one of the most familiar concepts in biostatistics and research design. It is supposed to help determine whether a study can detect a meaningful effe

New Schedule Update: Introducing Triple-Drop Wednesdays

We’re making a small but important update to the podcast schedule. To focus on delivering higher-quality, more in-depth content, we’re moving to a new release format: Tr

You’ve Been Using Predictive Models Wrong — Here’s What Actually Happens

Predictive models are widely used to identify high-risk patients and populations. They promise earlier intervention, better resource allocation, and improved outcomes. B

This Is Why Outbreak Curves Don’t Work (And Nobody Talks About It)

Outbreak curves are one of the most recognizable tools in epidemiology. They appear to show whether an epidemic is rising, peaking, or falling in real time. But what if t

In Theory, Statistical Significance Works. In Reality… It Doesn’t

Statistical significance is one of the most familiar ideas in research. It is often treated as the dividing line between real evidence and random noise. But what if that

Everyone Uses Subgroup Analysis… But It Fails When the Study Was Never Built for It

Subgroup analysis is one of the most persuasive tools in biostatistics and clinical research. It promises to show who benefits most, who responds differently, and where a